Mihalis M. Golias

TL;DR: In this article, a methodology to optimize truck arrival patterns to reduce emissions from idling truck engines at marine container terminals is proposed, where a bi-objective model is developed minimizing both truck waiting times and truck arrival pattern change.

TL;DR: Examples problems using real data show that the proposed policy reduces the amount of emissions produced by vessels at the port in idle mode, optimizes fuel consumption and waiting time at theport by reducing vessel operating speeds to optimal levels and minimizes the effects of late arrivals to the ocean carriers’ schedule.

TL;DR: In this paper, a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a representative sample of 483 consumers in Portland was performed, revealing six underlying consumer segments: Direct Shoppers, E-Shopping Lovers, COVID converts, Omnichannel Consumers, e-shopping Skeptics, and Indifferent Consumers.

TL;DR: The proposed model provides a robust berth schedule by minimizing the average and the range of the total service times required for serving all vessels at a marine container terminal by solving the discrete berth scheduling problem.

TL;DR: In this paper, a bi-objective bi-level optimization problem is formulated and a genetic algorithm based heuristic is discussed to find Pareto efficient schedules, which is compared to first-come-first-served policies.